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pandas replace values in column based on condition

In [41]:
df.loc[df['First Season'] > 1990, 'First Season'] = 1
df

Out[41]:
                 Team  First Season  Total Games
0      Dallas Cowboys          1960          894
1       Chicago Bears          1920         1357
2   Green Bay Packers          1921         1339
3      Miami Dolphins          1966          792
4    Baltimore Ravens             1          326
5  San Franciso 49ers          1950         1003
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pandas replace values based on condition

df.loc[df['First Season'] > 1990, 'First Season'] = 1
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replace values in a column by condition python

df.loc[df['employrate'] > 70, 'employrate'] = 7
Comment

replace column values/create new column based on another column values/condition in Pandas

df['New Column'] = np.where(df['A']==0, df['B'], df['A'])
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panda replace row values based on condition in some existing column

df['column'].mask(df['column'] == 'original_value', new_value, inplace=True)
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replace pandas column values based on condition

d.loc[d["conditionDisplayName"] == "Brand New", "conditionDisplayName"] = 6
d.loc[d["conditionDisplayName"] != "Brand New", "conditionDisplayName"] = 4
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